Intensity-based image and template matching is briefly reviewed with particular emphasis on the problems that arise when flexible templates or models are used. Use of such models and templates may often lead to a very small basin of attraction in the error landscape surrounding the desired solution and also to spurious, trivial solutions. Simple examples are studied in order to illustrate these problems which may arise from photometric transformations of the template, from geometric transforms of it or from internal parameters of the template that allow similar types of variation. It is pointed out that these problems are, from a probabilistic point of view, exacerbated by a failure to model the whole image, i.e. both the foreground object or template and the image background, which a Bayesian approach strictly requires. Some general remarks are made about the form of the error landscape to be expected in object recognition applications and suggestions made as to optimisation techniques that may prove effective in locating a correct match. These suggestions are illustrated by a preliminary example.